Data parallelism and halo exchange for distributed machine learning

ABSTRACT

One embodiment provides for a method of transmitting data between multiple compute nodes of a distributed compute system, the method comprising multi-dimensionally partitioning data of a feature map across multiple nodes for distributed training of a convolutional neural network; performing a parallel convolution operation on the multiple partitions to train weight data of the neural network; and exchanging data between nodes to enable computation of halo regions, the halo regions having dependencies on data processed by a different node.

CROSS-REFERENCE

This application claims priority to India provisional patent applicationnumber 201741015861, filed on May 5, 2017, which is hereby incorporatedherein by reference.

FIELD

Embodiments relate generally to data processing and more particularly todata processing via a general-purpose graphics processing unit.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SLIT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

BRIEF DESCRIPTION OF THE DRAWINGS

So that the features of the present invention can be understood indetail, a more particular description of the invention may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments and are therefore not to beconsidered limiting of the scope of all embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 2A-2D illustrate parallel processor components, according to anembodiment;

FIGS. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments;

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs is communicatively coupled to a plurality of multi-core processors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6 illustrates a machine learning software stack, according to anembodiment;

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit, according to an embodiment;

FIG. 8 illustrates a multi-GPU computing system, according to anembodiment;

FIG. 9A-9B illustrate layers of exemplary deep neural networks;

FIG. 10 illustrates an exemplary recurrent neural network;

FIG. 11 illustrates training and deployment of a deep neural network;

FIG. 12 is a block diagram illustrating distributed learning;

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model;

FIGS. 14A-14E illustrate communication patterns used during distributedmachine learning compute operations performed across multiple computenodes, according to embodiments described herein;

FIG. 15A-15C illustrate architectural details of the machine learningscaling library provided by embodiments described herein;

FIGS. 16A-16B illustrates distributed machine learning training enabledby embodiments described herein;

FIG. 17 illustrates distributed machine learning data transfers enabledby embodiments described herein;

FIGS. 18-21 illustrate flow diagrams that describe operations to enabledistributed machine learning via the MLSL API;

FIG. 22 is a block diagram of a data processing system, according toembodiments described herein;

FIG. 23 is a block diagram of a processing system, according to anembodiment;

FIG. 24 is a block diagram of a processor according to an embodiment;

FIG. 25 is a block diagram of a graphics processor, according to anembodiment;

FIG. 26 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments;

FIG. 27 is a block diagram of a graphics processor provided by anadditional embodiment;

FIG. 28 illustrates thread execution logic including an array ofprocessing elements employed in some embodiments;

FIG. 29 is a block diagram illustrating a graphics processor instructionformats according to some embodiments;

FIG. 30 is a block diagram of a graphics processor according to anotherembodiment;

FIG. 31A-31B illustrate a graphics processor command format and commandsequence, according to some embodiments;

FIG. 32 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments;

FIG. 33 is a block diagram illustrating an IP core development system,according to an embodiment;

FIG. 34 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment;

FIG. 35 is a block diagram illustrating an additional graphicsprocessor, according to an embodiment; and

FIG. 36 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit, according to anembodiment.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral-purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that can include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s) 112, memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212. In oneembodiment the scheduler 210 is implemented via firmware logic executingon a microcontroller. The microcontroller implemented scheduler 210 isconfigurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on the processing array 212. Inone embodiment, the host software can provide workloads for schedulingon the processing array 212 via one of multiple graphics processingdoorbells. The workloads can then be automatically distributed acrossthe processing array 212 by the scheduler 210 logic within the schedulermicrocontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment the memory crossbar 216 has aconnection to the memory interface 218 to communicate with the I/O unit204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample and in one embodiment, some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2A) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110 of FIG. 1,routed for further processing by the processor(s) 102, or routed forfurther processing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 248) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 248.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile and optionally a cache lineindex. The MMU 245 may include address translation lookaside buffers(TLB) or caches that may reside within the graphics multiprocessor 234or the L1 cache or processing cluster 214. The physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. The cache line index may beused to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 234. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 234. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 234.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 234. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 234 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can be executed via a single SIMDinstruction. For example and in one embodiment, eight SIMT threads thatperform the same or similar operations can be executed in parallel via asingle SIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 234to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIG. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440A-440D (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440A-440D support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 442A-442B, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440A-440D. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 443 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects430A-430B, respectively, and each GPU 410-413 is communicatively coupledto GPU memory 420-423 over GPU memory interconnects 450A-450D,respectively. The memory interconnects 430A-430B and 450A-450D mayutilize the same or different memory access technologies. By way ofexample, and not limitation, the processor memories 401-402 and GPUmemories 420-423 may be volatile memories such as dynamic random accessmemories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR)(e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may benon-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment,some portion of the memories may be volatile memory and another portionmay be non-volatile memory (e.g., using a two-level memory (2LM)hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 456may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the high-speed link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneembodiment, the accelerator integration circuit 436 includes a fetchunit 491 to fetch commands, instructions, work descriptors, etc., thatdefine operations to be performed. In one implementation, a cache 438stores commands and data for efficient access by the graphics processingengines 431-432, N. In one embodiment, the data stored in cache 438 andgraphics memories 433-434, M is kept coherent with the core caches462A-462D, 456 and system memory 411. As mentioned, this may beaccomplished via proxy circuit 425 which takes part in the cachecoherency mechanism on behalf of cache 438 and memories 433-434, M(e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over the high-speed link 440,biasing techniques are used to ensure that the data stored in graphicsmemories 433-434, M is data which will be used most frequently by thegraphics processing engines 431-432, N and preferably not used by thecores 460A-460D (at least not frequently). Similarly, the biasingmechanism attempts to keep data needed by the cores (and preferably notthe graphics processing engines 431-432, N) within the caches 462A-462D,456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 464 and caches462A-462D, 456.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 448 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of registers445 of the accelerator integration slice 490.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2C) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 2A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2A). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2A,and/or system memory 104 as in FIG. 1), to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 6 is a generalized diagram of a machine learning software stack600. A machine learning application 602 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 602 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 602can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 602 can beenabled via a machine learning framework 604. The machine learningframework 604 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 604, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 604. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 604 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 604 can process input data received fromthe machine learning application 602 and generate the appropriate inputto a compute framework 606. The compute framework 606 can abstract theunderlying instructions provided to the GPGPU driver 608 to enable themachine learning framework 604 to take advantage of hardwareacceleration via the GPGPU hardware 610 without requiring the machinelearning framework 604 to have intimate knowledge of the architecture ofthe GPGPU hardware 610. Additionally, the compute framework 606 canenable hardware acceleration for the machine learning framework 604across a variety of types and generations of the GPGPU hardware 610.

GPGPU Machine Learning Acceleration

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit 700, according to an embodiment. In one embodiment thegeneral-purpose processing unit (GPGPU) 700 can be configured to beparticularly efficient in processing the type of computational workloadsassociated with training deep neural networks. Additionally, the GPGPU700 can be linked directly to other instances of the GPGPU to create amulti-GPU cluster to improve training speed for particularly deep neuralnetworks.

The GPGPU 700 includes a host interface 702 to enable a connection witha host processor. In one embodiment the host interface 702 is a PCIExpress interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU700 receives commands from the host processor and uses a globalscheduler 704 to distribute execution threads associated with thosecommands to a set of compute clusters 706A-706H. The compute clusters706A-706H share a cache memory 708. The cache memory 708 can serve as ahigher-level cache for cache memories within the compute clusters706A-706H.

The GPGPU 700 includes memory 714A-714B coupled with the computeclusters 706A-706H via a set of memory controllers 712A-712B. In variousembodiments, the memory 714A-714B can include various types of memorydevices including dynamic random access memory (DRAM) or graphics randomaccess memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory, or 3Dstacked memory, including but not limited to high bandwidth memory(HBM).

In one embodiment each compute cluster 706A-706H includes a set ofgraphics multiprocessors, such as the graphics multiprocessor 400 ofFIG. 4A. The graphics multiprocessors of the compute cluster multipletypes of integer and floating point logic units that can performcomputational operations at a range of precisions including suited formachine learning computations. For example and in one embodiment atleast a subset of the floating point units in each of the computeclusters 706A-706H can be configured to perform 16-bit or 32-bitfloating point operations, while a different subset of the floatingpoint units can be configured to perform 64-bit floating pointoperations.

Multiple instances of the GPGPU 700 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment the multiple instances of the GPGPU 700 communicate over thehost interface 702. In one embodiment the GPGPU 700 includes an I/O hub709 that couples the GPGPU 700 with a GPU link 710 that enables a directconnection to other instances of the GPGPU. In one embodiment the GPUlink 710 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 700. In one embodiment the GPU link 710 couples with a high speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In one embodiment the multiple instances of the GPGPU 700are located in separate data processing systems and communicate via anetwork device that is accessible via the host interface 702. In oneembodiment the GPU link 710 can be configured to enable a connection toa host processor in addition to or as an alternative to the hostinterface 702.

While the illustrated configuration of the GPGPU 700 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 700 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration the GPGPU 700 includes fewer of the computeclusters 706A-706H relative to the training configuration. Additionallymemory technology associated with the memory 714A-714B may differbetween inferencing and training configurations. In one embodiment theinferencing configuration of the GPGPU 700 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 8 illustrates a multi-GPU computing system 800, according to anembodiment. The multi-GPU computing system 800 can include a processor802 coupled to multiple GPGPUs 806A-806D via a host interface switch804. The host interface switch 804, in one embodiment, is a PCI expressswitch device that couples the processor 802 to a PCI express bus overwhich the processor 802 can communicate with the set of GPGPUs806A-806D. Each of the multiple GPGPUs 806A-806D can be an instance ofthe GPGPU 700 of FIG. 7. The GPGPUs 806A-806D can interconnect via a setof high-speed point-to-point GPU to GPU links 816. The high-speed GPU toGPU links can connect to each of the GPGPUs 806A-806D via a dedicatedGPU link, such as the GPU link 710 as in FIG. 7. The P2P GPU links 816enable direct communication between each of the GPGPUs 806A-806D withoutrequiring communication over the host interface bus to which theprocessor 802 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 800, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 806A-806D connect to the processor 802via the host interface switch 804, in one embodiment the processor 802includes direct support for the P2P GPU links 816 and can connectdirectly to the GPGPUs 806A-806D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIG. 9A-9B illustrate an exemplary convolutional neural network. FIG. 9Aillustrates various layers within a CNN. As shown in FIG. 9A, anexemplary CNN used to model image processing can receive input 902describing the red, green, and blue (RGB) components of an input image.The input 902 can be processed by multiple convolutional layers (e.g.,convolutional layer 904, convolutional layer 906). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 908. Neurons in a fully connected layer have fullconnections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 908 can be used to generate an output result from the network.The activations within the fully connected layers 908 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations are make use of fully connected layers 908. For example,in some implementations the convolutional layer 906 can generate outputfor the CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 908. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 9B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 912 of a CNN can beprocessed in three stages of a convolutional layer 914. The three stagescan include a convolution stage 916, a detector stage 918, and a poolingstage 920. The convolution layer 914 can then output data to asuccessive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 916 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 916 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 916defines a set of linear activations that are processed by successivestages of the convolutional layer 914.

The linear activations can be processed by a detector stage 918. In thedetector stage 918, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max (0,x), such that the activation is thresholded at zero.

The pooling stage 920 uses a pooling function that replaces the outputof the convolutional layer 906 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 920,including max pooling, average pooling, and l2-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 914 can then be processed by thenext layer 922. The next layer 922 can be an additional convolutionallayer or one of the fully connected layers 908. For example, the firstconvolutional layer 904 of FIG. 9A can output to the secondconvolutional layer 906, while the second convolutional layer can outputto a first layer of the fully connected layers 908.

FIG. 10 illustrates an exemplary recurrent neural network 1000. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1000 can bedescribed has having an input layer 1002 that receives an input vector,hidden layers 1004 to implement a recurrent function, a feedbackmechanism 1005 to enable a ‘memory’ of previous states, and an outputlayer 1006 to output a result. The RNN 1000 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1005. For agiven time step, the state of the hidden layers 1004 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first time step can be processed by the hidden layer 1004. Asecond input (x₂) can be processed by the hidden layer 1004 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t-1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tan h) or avariant of the rectifier function ƒ(x)=max(0,x). However, the specificmathematical function used in the hidden layers 1004 can vary dependingon the specific implementation details of the RNN 1000.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 11 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 1102. Various training frameworks1104 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework 604 of FIG. 6 maybe configured as a training framework 604. The training framework 604can hook into an untrained neural network 1106 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 1108.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1102 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1104 can adjust to adjust the weights that controlthe untrained neural network 1106. The training framework 1104 canprovide tools to monitor how well the untrained neural network 1106 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 1108. The trained neural network 1108 can then bedeployed to implement any number of machine learning operations togenerate an inference result 1114 based on input of new data 1112.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1102 will include input data without any associatedoutput data. The untrained neural network 1106 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1108 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1102 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1108 to adapt tothe new data 1112 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 12 is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly-parallel general-purpose graphicsprocessing unit 700 as in FIG. 700. As illustrated, distributed learningcan be performed model parallelism 1202, data parallelism 1204, or acombination of model and data parallelism 1204.

In model parallelism 1202, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks. In another example of modelparallelism, computation in one or more layers of a neural network modelcan be split across multiple compute nodes across feature map dimensionto reduce size of per node model parameters.

In data parallelism 1204, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1206 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit 700 of FIG. 700and the multi-GPU computing system 800 of FIG. 800. On the contrary,deployed machine learning platforms generally include lower powerparallel processors suitable for use in products such as cameras,autonomous robots, and autonomous vehicles.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC) 1300suitable for performing inferencing using a trained model. The SOC 1300can integrate processing components including a media processor 1302, avision processor 1304, a GPGPU 1306 and a multi-core processor 1308. TheSOC 1300 can additionally include on-chip memory 1305 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1300 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1300 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1302 and vision processor 1304 canwork in concert to accelerate computer vision operations. The mediaprocessor 1302 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 1305. The vision processor 1304 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1304 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 1306.

The multi-core processor 1308 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1302 and the visionprocessor 1304. The multi-core processor 1308 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1306. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1308. Such softwarecan directly issue computational workloads to the GPGPU 1306 or thecomputational workloads can be issued to the multi-core processor 1308,which can offload at least a portion of those operations to the GPGPU1306.

The GPGPU 1306 can include compute clusters such as a low powerconfiguration of the compute clusters 706A-706H within thehighly-parallel general-purpose graphics processing unit 700. Thecompute clusters within the GPGPU 1306 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 1306 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

Abstraction Layers for Scalable Distributed Machine Learning

Currently, data scientists that develop applications that make use ofdistributed deep learning are required to explicitly implement thecommunication system between the compute nodes. Implementing theunderlying communications system for distributed deep learning requiressome knowledge of distributed or networked compute node communicationtechniques, including the libraries required to implement suchtechniques. For example, to implement distributed deep learning modelssuch as data parallelism, model parallelism, or hybrid parallelism(mixed data and model parallelism), the application developer may berequired to explicitly construct the communication infrastructure usinglow level communication libraries, such as the message passing interface(MPI) library. The application developer will then be required todetermine the specific units of data to transfer and the specific nodesthat will be transmitting and receiving such information. As deeplearning application developers may not be domain specific experts inthe construction of distributed compute infrastructure, many bestpractices and optimizations may not be included in the communicationimplementation developed for a given deep learning application.

Distributed machine learning can be implemented using a variety ofparallelism patterns, such as data parallelism, model parallelism, or ahybrid of data and model parallelism, as illustrated in FIG. 12. Asdescribed with respect to FIG. 12, data parallelism uses the same modelfor each compute node, with each node processing different portions ofthe data. Model parallelism uses the same data for each compute node,with the model split among compute nodes.

To enable communication, multiple types of low-level communicationpatterns are used to transfer data between nodes. The low-levelcommunication patterns used are illustrated in Table 5 below.

TABLE 5 Low Level Communication Operation Communication OperationDescription GATHER Gathers data from multiple processes in a group intoa specified array in a single process SCATTER Distribute data from asingle array into multiple segments, where different segments are sentto different processes ALLGATHER Gather operation in which all processesreceive the gather result ALLTOALL Each process in the group sendsdistinct data to each receiver REDUCE A global reduction operation inwhich the outcome from applying some desired function across allprocesses in a group is collected in one specified processREDUCE_SCATTER Element-wise reduction on vector of element, with theresulting vector split into disjoint segments, with different segmentssent to each process in a group ALLREDUCE A reduce combined with abroadcast, where the outcome of the reduce operation is broadcast to allprocesses within a group.

FIGS. 14A-14E illustrate communication patterns used during distributedmachine learning compute operations performed across multiple computenodes, according to embodiments described herein. FIG. 14A illustratesdata transfer for machine learning computation using data parallelism.FIG. 14B illustrates data transfer for distributed machine learningcomputation using model parallelism. FIG. 14C illustrates partitioningof machine learning computation across multiple nodes using hybridparallelism. FIG. 14D illustrates distributed machine learningcomputation using hybrid parallelism across multiple nodes and acrossmultiple layers. FIG. 14E illustrates a set of exemplary messagingpatterns operations that may be used for distributed machine learning.In each of FIG. 14A-14E, input data 1402 is processed by a machinelearning model having a set of weights 1404 to generate a set ofactivations 1408 or partial activations 1406.

As shown in FIG. 14A, data parallelism can be implemented in which inputdata 1402 is split along a mini-batch dimension and the same model isreplicated across the nodes. The mini-batch is split across severalcompute nodes, with each node responsible for computing gradients withrespect to all model parameters using a subset of the samples in themini-batch. Forward propagation is performed independently on each node.In one embodiment only one communication is performed during thebackward pass to calculate an average for the gradients with respect tolearnable parameters. An all reduce operation 1405 is used to update theweights of each layer for the next forward pass. In one embodiment,distributed weight update can be enabled in which a reduce_scatter isused calculate an average for gradients before stochastic gradientdescent is performed and an allgather operation is used after stochasticgradient descent to synchronize weights across nodes.

As shown in FIG. 14B, model parallelism can be implemented in which themodel or set of weights is split across multiple nodes. Generally, modelparallelism performs different portions of a model's computation areperformed simultaneous on different nodes for the same batch ofexamples. For model parallelism, the input data is also split (e.g.,along the channel dimension), as shown in FIG. 14B. Using theillustrated approach, a reduce operation is performed to sum up theactivations to obtain the actual output and then scatter the activationsfor use in computing activations for the next layer. A reduce_scatter1407 operation can be performed to transfer the data in a singlecommunication operation. In the backward pass an allgather operation isperformed to combine strips of gradients computed on each node.

As shown in FIG. 14C, hybrid parallelism can be performed in which apartitioning is performed across activations and weights to minimizeskewed matrices. For a layer of a neural network, the input data 1402,weight data 1404, and/or activation data 1406 is partitioned anddistributed across multiple compute nodes (e.g., Node 0-Node 3). Node 0receives a first block of input data 1402A and weight data 1404A.Compute operations are performed at Node 0 to generate a first partialactivation 1406A. Likewise, Node1 receives a second block of input data1402B and weight data 1404B. Compute operations are performed at Node 1to generate a second partial activation 1406B. Node 2 can performcompute operations on third input data 1402C and weight data 1404C togenerate a third partial activation 1406C. Node 3 can perform computeoperations on fourth input data 1402D and weight data 1404D to generatea fourth partial activation 1406D.

FIG. 14D illustrates the transfer of partial activation data 1406A-1406Bfor a given layer of a neural network (Layer N−1) to a successive layerof the neural network (Layer N). Via multiple nodes (Node 0, Node 1), aset of partial activations 1406A-1406B is generated by based on theapplication of a mathematical operation (e.g., convolution) to the inputdata 1402A-1402B and weight data 1404A-1404B. For example and in oneembodiment, a reduce_scatter operation 1410 is used which performs areduce operation on the partial activations 1406A-1406B of layer N-1from the multiple nodes and scatters the result to the multiple nodes asactivations for use in Layer N of the neural network.

FIG. 14E illustrates the exemplary communication operations used totransfer data for distributed training of a neural network for machinelearning operations. The low-level messaging libraries are used toenable data transfers for weight and activation data during distributedtraining of a neural network. An exemplary neural network having Nlayers 1421A, 1421B, 1421N (e.g., Layer 1, Layer 2, through Layer N) canbe trained in a distributed manner by performing successive forwardcompute operations on the successive layers to enable forwardpropagation 1426 of activation data through the neural network. Duringforward propagation 1426, an Alltoall 1409 communication operation isused to transfer activation data from a first layer 1421A to asuccessive layer 1421B, for example, where the first layer and thesuccessive layer are hidden layers or non-output layers. The Alltoall1409 operation transfers distinct data from the compute nodes thatgenerate the activation or partial activation data to all availablereceivers, which use the activation data as input data for operations onsuccessive layers. When transferring data final layers (e.g., layer N),the reduce scatter operation 1410 is performed, which is described withrespect to FIG. 14B. During back propagation 1428, distributedstochastic gradient descent is performed to generate updated weightdata. An initial Allreduce operation 1412 is performed for Layer N and aset of Allreduce operations 1411A, 1411B, 1411N are performed to updatethe weights of each layer for the next forward pass. The Allreduceoperations are reduce operations for which the results are broadcast ortransferred to the receive buffers of all processes in the communicationgroup. The back propagation 1428 can also include Allgather 1413 andAlltoall 1414 communication operations. For the Allgather operation 1413data is gathered from all tasks and the combined data is distributed toall tasks. For the Alltoall operation 1414 data from all processes istransferred to all processes.

The data transfers required to perform distributed compute operationsfor machine learning can be implemented using any low-level messaginglibrary, such as MPI, gRPC, or zeroMQ. However, implementing theexemplary communication operations may be difficult without domain levelexpertise of multiprocessor communications libraries. Furthermore,scaling these operations to a very large number of nodes can bedifficult. Without domain specific knowledge of distributed computingtechniques, implementing a scalable communication system for machinelearning that can handle communication between hundreds or thousands ofnodes may significantly extend development time for machine learningapplications.

Embodiments described herein provide various techniques to abstract thedistributed communication system detail for a deep learning application.In one embodiment a machine learning scaling library (MLSL) is providedthat enables deep learning application developers to develop distributeddeep learning applications without requiring knowledge of the specificcommunication details required to enable multi-node deep learning. Anapplication developer for a deep learning application can specify, usingdeep learning or machine learning domain specific terminology, the typeof distributed compute system that is used by an application and librarytechniques provided by embodiments described herein can implement thespecific underlying communication methods required to enable therequested distributed compute system.

FIG. 15A-15C illustrate architectural details of the machine learningscaling library provided by embodiments described herein. FIG. 15Aillustrates an exemplary machine learning architecture stack 1500. FIG.15B illustrates details of the MLSL architecture 1511. FIG. 15Cillustrates exemplary communications endpoints enabled by embodiments.

FIG. 15A illustrates an exemplary machine learning architecture stack1500, which may be a variant of the machine learning software stack 600of FIG. 6. The machine learning architecture stack 1500 includesmultiple software and hardware layers that range from input data 1502provided by an array of sensors to hardware 1514 elements that performvarious compute, storage, or communication operations. Each layer of theexemplary machine learning architecture stack 1500 may be an opaqueabstraction layer that hides implementation details from higher layers,while using functionality provided by lower layers to implement thefunctions required by the higher layers.

Input data 1502 is provided to a layer of applications 1504. In oneembodiment the input data 1502 is multi-modal input including but notlimited to video and/or image data, data from multiple sensors, andexternal signal data. The applications 1504 include multi-modal fusionand decision-making applications that can process the input to enablemachine learning tasks such as image understanding, video summarization,speech and natural language processing, path planning, navigation, orany other machine learning implementation described herein. Theapplications 1504 communicate with one or more machine learningframeworks 1506, such as but not limited to Caffe, Theano, Torch,TensorFlow, or any other scripting based machine learning framework, toimplement machine learning specific operations. The machine learningframeworks 1506 can enable machine learning operations to be performedusing one of any number of neural network topologies 1508, including butnot limited to a CNN, RNN, LSTM, Generic Deep Neural Networks, andReinforcement Learning Networks. Machine learning frameworks 1506implement the neural network topologies 1508 via one or more buildingblocks 1510. Exemplary building blocks 1510 include the single precisionfloating general matrix multiply (SGEMM) block, convolution buildingblocks, Fast Fourier transform/Winograd blocks, single-sourceshortest-path (SSSP) computation blocks, sparse matrix-matrixmultiplication (SpGEMM) blocks, and the machine learning scaling library(MLSL) 1511 provided by embodiments described herein. The buildingblocks 1510 can each implement multiple algorithms 1512 to enable thecompute operations requested by the frameworks 1506 to implement theneural network topologies 1508. The algorithms 1512 includeoptimizations to enhance statistical and architectural efficiency,enable cloud deployment, and enable scaling to a large number of nodes.In one embodiment the MLSL 1511 includes algorithms 1512 to enablescaling of machine learning operations to a large number of nodes. Inone embodiment the building blocks 1510 can be implemented via softwarelibraries that may be accelerated by one or more elements of thehardware 1514. In one embodiment at least a portion of the buildingblocks 1510 may be implemented within hardware 1514. For example, FPGAor ASIC based accelerators can include custom logic to enable portionsof the MLSL 1511 or one or more GEMM libraries.

Various components of the hardware 1514 can be used to implementfunctionality of higher layers of the machine learning architecturestack 1500. Components of the hardware 1514 include, but are not limitedto a CPU or another general-purpose processor tasked with performingcomputational and/or operating system related computations. The hardware1514 also includes a many integrated core (MIC) or general-purpose GPUbased parallel processing system. In some embodiments the hardware 1514includes FPGA or ASIC based deep learning accelerators. A fabricinterconnect component of the hardware 1514 is used to enable high-speedcommunication between the various components and high-bandwidth volatileor non-volatile memory. The volatile memory technologies can include anyof the graphics memory technologies described herein, including HBM andGDDR memory. The non-volatile memory technologies can include flashmemory, including 3D NAND flash, or other memory technologies such as 3DXpoint memory.

FIG. 15B illustrates details of the MLSL architecture 1511, according toembodiments. The MLSL architecture 1511 includes an abstraction layerhaving machine-learning-specific abstractions 1513 as well asnon-machine-learning specific abstractions 1515. The abstractionsinterface with a communication module 1517 that drives an underlyingmessaging library 1519. The messaging library 1519 uses optimizedlow-level communication routines to transmit data over ahigh-performance communications fabric 1521.

The MLSL architecture 1511 enables developers of machine learningsoftware to develop scalable machine learning applications using machinelearning specific abstractions 1513. In one embodiment the machinelearning specific abstractions 1513 enable an application developer touse machine learning domain specific knowledge to drive scalableperformance for compute operations for neural network layers. Themachine learning abstractions 1513 enable applications to be developedin a manner that is transparent to the underlying architecture, enablingmachine learning applications to automatically adapt to any number ofthe hardware 1514 elements, including multiple types of compute andfabric elements. In addition to the machine learning specificabstractions 1513, a set of non-machine-learning specific abstractions1515 can also be provided by the MLSL architecture 1511. Thenon-machine-learning specific abstractions 1515 enable a developer of amachine learning application to define, at a higher level ofabstraction, one or more non-machine-learning details of theapplication, such as one or more implementation specific details oroperating system details that are unrelated to machine learning.

In one embodiment, the machine learning specific abstractions 1513enable neural network layer appropriate support for multiple types ofparallelism (e.g., data, machine, hybrid). The machine learning specificabstractions 1513 also enable Layer-to-Layer communication abstractionsto allow developers to easily implement communication patterns fordifferent layer types and parallelisms. The different layer types andparallelism are defined using machine learning specific terminologyusing the machine learning specific abstractions 1513 and communicationfor those layer types are enabled via the communication module 1517, themessaging library 1519, and the high-performance communications fabric1521. The machine learning specific abstractions 1513 also enableintelligent message scheduling across the defined neural network layers,while abstracting the data layouts and transformations required toimplement machine learning techniques at the application level.

In one embodiment, the communication module 1517 includes logic to drivethe underlying messaging library 1519. The communication module 1517includes various optimizations to enable the network to be drivenefficiently while transmitting machine learning data between the variouscompute nodes used to perform distributed machine learning. Thecommunication module 1517 includes logic to optimize network bandwidthand to enable the low latency communications. The machine learningspecific abstractions 1513 and/or the non-machine-learning specificabstractions 1515 can specify or prove interfaces to enable theapplication developer to specify the processor resources tasked withmanaging distributed communication. In one embodiment specificprocessors can be specified. In one embodiment, the number of processorassociated with communication are specified. In one embodiment, a mixbetween compute and communication resources can be specified. In oneembodiment, the communication module 1517 includes logic to adaptivelyassign processor cores for use in driving and performing operations forthe communication module 1517 and/or the messaging library 1519. In oneembodiment the communication module 1517 can adaptively assignprocessing resources for communication without explicit direction fromthe machine learning specific abstractions 1513 or thenon-machine-learning specific abstractions 1515. In one embodiment thecommunication module 1517 can adaptively adjust or assign processingresources to attempt to fully saturate available network resources toattempt to minimize the latency impact of communication within thedistributed system. For example, should the communication module 1517determine that the high-performance communication fabric 1521 is notfully saturated with data, additional processors or processor cores canbe assigned to perform network tasks if overall throughput of thedistributed compute system would be increased. In one embodiment theamount of compute resources assigned to drive the messaging library 1519can vary based on the bandwidth of the high-performance communicationsfabric. For higher-bandwidth fabric, greater computational resources maybe required to saturate the network. The high-performance communicationsfabric 1521 can be implemented via any number of high-speed networkconnection technologies, including but not limited to Ethernet,InfiniBand, Omni-Path Interconnect, or proprietary technologies such asNvLink.

In one embodiment the communication module 1517 includes logic to ensureforward progress of distributed compute operations by enablingasynchronous communication between processing nodes. The asynchronouscommunication enabled by the communication module 1517 allowsoverlapping compute and communication operations that efficientlyinterleave to optimize both compute and communication efficiency andthroughput. For example, the interleaved compute and communicationoperations can be configured to hide latency events within compute andcommunication operations, such that idle compute resources resultingfrom blocked compute operations can be used to accelerate communicationoperations, while idle compute resources resulting from blockedcommunication operations can be used to accelerate compute operations.In one embodiment the communication module 1517 also supportsprioritized communication channels to enable prioritized resolution ofcontending communication requests.

The messaging library 1519 uses optimized low-level communicationroutines to transmit data over a high-performance communications fabric1521. The MLSL architecture 1511 is agnostic with respect to theunderlying messaging library 1519 and high-performance communicationsfabric 1521. In one embodiment the messaging library is an MPI-basedlibrary. In such embodiment the communication patterns used by a machinelearning application are implemented using MPI functions (e.g., MPIAlltoall, MPI Allreduce, MPI Allgather, etc.). In some embodiments thegRPC or zeroMQ libraries and associated functions are used formessaging. In one embodiment the NCCL collective communications routinesmay also be used. NCCL provides communication routines such asall-gather, reduce, and broadcast to accelerate multi-GPU machinelearning training across multiple GPGPUs.

FIG. 15C illustrates exemplary communications endpoints enabled byembodiments described herein. The concepts provided by these embodimentsis illustrated with respect to the MPI library, although the techniquesdescribed are not limited to MPI implementations. In a conventionalcommunicator 1525, a process is associated with a rank or anothercommunication ID. The process can support communication for multiplethreads, each thread associated with the rank or identifier of theprocess. Embodiments described herein make use of network endpoints toenable communication between the various compute nodes of a distributedcompute system. Each endpoints communicator 1530 allows a flexiblearrangement between process, a communication rank or ID, and the variousthreads that use the endpoint for communication. The endpointscommunicator 1530 can be dynamically configured, such that a process canbe associated with multiple ranks and each rank can be associated with aseparate process. In such configuration, each thread can send data viathe multiprocessor messaging system without regard to thread contentionamong ranks. Alternatively, a thread can be associated with multipleranks, enabling a single thread to have multiple communication channels.

In one embodiment, one or more instances of the endpoints communicator1530 is explicitly specified via the machine learning specificabstractions 1513. In one embodiment the number of instances of theendpoints communicator 1530 is directly related to the number of coresassigned to perform network communication. In one embodiment the machinelearning specific abstractions 1513 enable a programmer to specify,using machine learning specific terminology, the type of network and thedegree of parallelism required and the MLSL architecture 1511 candynamically construct the communications infrastructure, including thenumber of cores assigned to networking operations and the associatednumber of communications endpoint 1530.

In various embodiments the communications system of FIG. 15C can beconstructed using explicitly developer defined variables or dynamicallyconstructed based on the machine learning infrastructure defined by theapplication developer. In one embodiment a machine learning applicationcan define multiple application processes 1536 that perform computeoperations for the machine learning application. The MLSL 1534 canexpose interfaces to the application processes 1536 that enables acommunications system that is scalable to a very large number of computenodes. In such configuration, multiple communications ranks oridentifiers are supported for each of the application processes 1536(e.g., Process 0, Process 1, Process 2), which, in one embodiment, mayeach be MPI processes. A set of endpoint processes 1532 can be initiatedby the MLSL 1534, with separate endpoint process defined to support eachrank or identifier of the processes within the application processes1536. In one embodiment, machine learning specific domain awareness canbe combined with a global view of communication operations to determinehow many endpoints to use. The number of endpoint processes 1532 can bescaled dynamically by the MLSL 1534 based on communication needs.

FIGS. 16A-16B illustrates distributed machine learning training enabledby embodiments described herein. FIG. 16A illustrates a training processfor a neural network that is performed using multiple nodes. An MLSL APIcan be used to define a distributed training system including multiplenodes. In one embodiment the multiple nodes can include a first node1610 (Node 0) and a second node 1620 (Node 1). Each node 1610, 1620 isconfigured to perform forward compute operations 1612, 1622 and backwardcompute operations 1614, 1624. For the backward compute operations 1614,1624, weight deltas 1616, 1626 are computed and stochastic gradientdescent 1618, 1628 is performed to generate weight value updates. Thecommunication operations enabled by the MLSL API are illustrated asletter/number blocks that perform operations shown in Table 6.

TABLE 6 MLSL Communication Operations Communication Action CommunicationPhase 1. Activation a. Start Communication 2. Activation Gradients b.Wait to Finish Communication 3. Weight Gradients 4. Updated Weights

As illustrated in FIG. 16A, the MLSL API enables forward propagationusing distributed forward compute operations 1612, 1622 that arebracketed by a first communication block that waits to finishcommunication for incoming data before beginning the forward computeoperations and a second communication block that starts communicationfor computed data. For example, a developer can use a first MLSL APIcommand (Node 0 [1b]) to configure the forward compute 1612 for a firstlayer at the first node 1610 to wait to finish receiving communicationof activation data that will be used as input data for the forwardcompute 1612. The forward compute 1612 automatically begins uponcompletion of the communication of the activation data. Upon completionof the forward compute 1612, a second MLSL API command (Node 0 [1a]) canbe used to start communication of activation data. The communicatedactivation data output from the first node 1610 is activation datagenerated by the first layer and is used as input data for a secondlayer having a forward compute 1622 performed at the second node 1620.The forward compute 1622 at the second node 1620 waits to finishcommunication of activation data before beginning compute operations(Node 1[1b]) and, upon completion, starts communication of activationdata (Node 1[1b]) generated by the forward compute 1622.

In one embodiment, the MLSL API enables backward propagation usingdistributed backward compute operations 1624, 1612 that are bracketed bya third MLSL API enabled communication block (Node 1[2b]) that waits tofinish communication for incoming activation gradients before beginningthe backward compute operation 1624 and a fourth MLSL API enabledcommunication block (Node1[2a]) that starts communication for computedactivation gradients. In a similar manner, the MLSL API enablestransmission and receipt of weight gradients for weight delta computes1626, 1616 and updated weights determined via distributed stochasticgradient updates 1628, 1618.

As illustrated in FIG. 16B, each node 1610, 1620 can also be used toperform compute operations for multiple layers of a neural network. Inone embodiment the forward compute operations 1612, 1622 shown in FIG.16A are performed as multiple compute operations 1612A-1612B,1622A-1622B across multiple layers (Layer N, Layer N+1). Likewise thebackward compute operations 1624, 1614 shown in FIG. 16A can beperformed as multiple compute operations 1624A-1624B, 1614A-1614B. Foreach node 1610, 1620, the MLSL API can enable activations 1602, 1604 tobe transferred between the multiple neural network layers on each node,while updated weights 1606, 1608 are distributed after backward computeoperations 1624A-1624B, 1614A-1614B.

In one embodiment the MLSL API enables the use different types ofparallelization for different layers of the same neural network. Thechoice of parallelism can be made automatically by the MLSL based onlayer properties, such as the number of learnable parameters and thenumber of activations. Based on the parallelism determined for layers,the type of communication required can also be determined. For example,when the previous layer uses data parallelism and the next layers usesmodel parallelism, an all-to-all communication pattern is invoked toredistribute the data. Generally, the variance of communication patternsand scenarios is significant. By abstracting the communication details,the MLSL API can significantly simplify the life of machine learningframework developers. Various machine learning structures can beimplemented via the MLSL API.

MLSL Programming Model

In one embodiment the MLSL API enables a machine learning applicationdeveloper to describe communication dependencies between computationaloperations. Once the data dependencies are described, the MLSL canperform the required data exchanges. In one embodiment the MLSL operateson the objects shown in Table 7.

TABLE 7 MLSL Main Objects Object name Description ParameterSet An objectthat holds information about the shape of learnable parameters andallows performing communication exchanges with respect to it ActivationAn object that holds information about the shape of the activation andallows performing communications with respect to it Operation An objectthat holds information about learnable parameters and activationscorresponding to a certain computational operation Session Represents acollection of Operation's objects with the same batch size DistributionAn object that holds information about the parallelism scheme used(data, model, or hybrid parallelism)

The MLSL main objects include a ParameterSet object, an Activationobject, an Operation object, a Session object, and a Distributionobject, as described above. In the MLSL terms, the parameter set is aset of equally sized kernels, the activation is a set of equally sizedfeature maps. The MLSL also provides auxiliary objects, as shown belowin Table 8

TABLE 8 MLSL Auxiliary objects Object name Description OperationRegInfoAn object that holds information about learnable parameters andactivations shapes and is used for creation of an Operation objectCommBlockInfo An object that holds information about Activation'spartitioning and is used for packing/unpacking to/from the communicationbuffer Environment A singleton object that holds global MLSL functions

The MLSL auxiliary objects include a OperationRegInfo object, aCommBlockInfo object, and an Environment object, as described above. TheMLSL API can be used to set up a distributed compute environment usingmachine learning specific information. Exemplary setup logic is shown inTable 9 below.

TABLE 9 MLSL Setup Logic Environment &env = Environment::GetEnv( );env.Init(&argc, &argv); Session* session = env.CreateSession( );session−>SetGlobalMinibatchSize(GLOBAL_MINIBATCH_SIZE); Distribution*distribution = env.CreateDistribution(processCount/groupCount,groupCount); OperationRegInfo* regInfo =session−>CreateOperationRegInfo(OT_CC); regInfo−>SetName(“MyLayerName”);regInfo−>AddInput(lParams−>ifm, lParams−>ifmWidth * lParams−>ifmHeight,MLSL_DTYPE); regInfo−>AddOutput(lParams−>ofm, lParams−>ofmWidth *lParams−>ofmHeight, MLSL_DTYPE); regInfo−>AddParameterSet(lParams−>ifm *lParams−>ofm, lParams−>kw * lParams−>kh, MLSL_DTYPE, useDistUpdate);size_t opIdx = session−>AddOperation(regInfo, distribution);session−>DeleteOperationRegInfo(regInfo); Operation* op =session−>GetOperation(opIdx);

The exemplary logic of Table 9 illustrates use of the MLSL API to setupdistributed computation for a neural network using machine learningdomain specific knowledge. A new distribution can be created using auser specified number of nodes and/or groups. Neural network layers canbe defined along with input feature map, output feature map, and weightdata.

Exemplary forward propagation logic enabled via the MLSL API is shownTable 10 below.

TABLE 10 MLSL Forward Propagation Logic void Forward( ) { Activation*act = op−>GetInput(0); DTYPE* commBuf = (DTYPE*)act−>WaitComm( );UnpackBuffer(act, commBuf, inputActBuf); if (op−>HasParameterSets( )) {op−>GetParameterSet(0)−>WaitIncrementComm( ); }ForwardCompute(inputActBuf, paramBuf, outputActBuf); act =op−>GetOutput(0); DTYPE* outputActCommBuf = (DTYPE*)act−>GetCommBuf( );PackBuffer(act, outputActCommBuf, outputActBuf);act−>StartComm(outputActCommBuf); }

The exemplary forward propagation logic of Table 10 illustrates use ofthe MLSL API to enable automatic data communication during forwardpropagation. The illustrated forward propagation logic of Table 10corresponds with the illustrated forward compute and data transmissionsillustrated in FIGS. 16A-16B. Communications that depend on data fromother processes, or nodes can be gated via a CommsWaitWtInc functioncall. Compute operations can be performed with the dependency issatisfied. A forward propagation compute operation can be performed andthe output data can be communicated to the necessary consumers. In oneembodiment the data relationships are described via machine learningdomain specific definitions enabled via the MLSL API and the low-levelcommunication techniques required to enable the communication areperformed automatically.

Data Parallelism and Halo Exchange for Distributed Machine Learning

The machine learning scaling logic and techniques descried herein may beused to accelerate numerous machine learning problem sets. Embodimentsdescribed herein are configurable to enable data parallelism for use inaccelerating halo data exchange when performing machine learningoperations, for example, to enable overlapping compute and dataoperations on each of the distributed nodes.

In neural networks configured to accept large inputs in the megapixeland gigapixel range, many optimization tasks are performed to maintainhigh data resolution throughout the depth of the neural network. Computeoperations on high resolution image input data are typically performedusing small mini-batch sizes, as a single large input can be consideredto be a combination of multiple smaller input images. However, smallsized mini-batches may not sufficiently leverage the parallel processingcapabilities of a GPGPU compute node or a multi-node parallel processingsystem.

A large feature map such as a megapixel or gigapixel image may have Xand Y dimensions that may be split into multiple smaller portions in theZ direction. Thus, in conventional approaches the neural network isdownsampled from, for example, 256×256 pixels to 64 feature maps of112×112 pixels in a second layer. However, there are many instanceswhere downsampling loses resolution that is critical to the performanceof the machine learning system. One example is cancer detection, wherethe image size may be on the order of 2000×2000 pixels.

Embodiments described herein enhance the compute parallelism for largesize inputs by splitting the feature maps in the X and Y dimensionsinstead of the Z-dimension, which is typically used when performingdistributed computing using model parallelism. Such embodiments enhanceparallelism by leveraging the large ratio of feature map size to kernelsize. Performing model parallel compute of input data split along the Xand Y dimensions results in a hybrid 2D-domain decomposition modelparallelism plus data parallelism. In one embodiment the halo regionsare computed and distributed before the internal regions. Compute anddata transmission can be performed in an overlapping manner.

Whether to enable model or data parallelism for a given dataset may bedetermined based on the size of the feature maps and the number ofimages being processed in parallel. If a large amount of feature mapdata is to be processed, due to the large size of the feature maps, thendata parallelism is preferred. For CNNs, data parallelism may also beefficiently performed for smaller sized feature maps for convolutionallayers, although model parallelism may be preferred for fully connectedlayers, as model parallelism my result in lower communication overhead.Additionally, using MLSL techniques described herein, hybrid parallelismcan also be used, which combines aspects of data and model parallelism.

Embodiments described herein are optimized for use cases involving largefeature maps. For example, the automated annotation use case make usesof large feature maps to determine the contents of a portion of an imageand apply a label to that portion of an image. Automated annotation canbe used to determine a freeform boundary of an object within an inputimage. In one implementation of automated annotation, the neural networkmodel does not perform downsampling through the successive layers of theneural network and each layer retains high resolution data. Aninferencing pass through the neural network can generate output in whicheach pixel of an input image is annotated with a classification or labelidentifying the object associated with the pixel.

For a given neural network layer M, the computation of the outputfeature map for layer M+1 will have a remote data dependency that isrepresented by the halo region. The halo region, for each node,represents data that must be received from neighboring nodes to completeprocessing.

In one embodiment, data transmission and compute are overlapped as muchas possible. Computations and/or data transmissions required to satisfydependencies on other nodes may be performed at each node beforeindependent computations are performed. In one embodiment, where datadependencies have not yet been satisfied for a layer, computations forthe layer that may be completed independently are performed whilewaiting for remote data dependencies to be satisfied.

FIG. 17A-17D illustrate logic to enable distributed machine learningdata transfers enabled by embodiments described herein. FIG. 17Aillustrates data regions associated with distributed training with X andY dimension partitioning. FIG. 17B illustrates data halo regionsassociated with convolution operations for a neural network. FIG. 17C isa sequence diagram of compute and data exchange, according to oneembodiment. FIG. 17D is a sequence diagram of compute and data exchange,according to an additional embodiment.

As shown in FIG. 17A, a large input feature map 1701 can be split alongan X and Y directions in addition or as an alternative to a Z-dimensionsplit 1705. Each region of the split feature map can be associated witha separate compute node. For example and in one embodiment a feature mapcan be split into four quadrants (Node 0 data 1710; Node 1 data 1720;Node 2 data 1730; Node 3 data 1740), with each quadrant associated witha separate node. The dimension of the quadrants can be defined based ona height (OH 1708) parameter and a width (OW 1706) parameter. Forvarious types of neural networks, such as, for example, a CNN, eachquadrant will include a halo region 1707 that defines a region of remotedata dependency. The nature of the data dependency can vary based on theimplementation details of the distributed training model for the neuralnetwork. In general, however, the halo region 1707 is a data region forwhich a data exchange is required between nodes before computations forthe quadrant can be completed.

FIG. 17B illustrates independent and remote data dependency regions ofthe various quadrants. For each layer of a CNN, forward propagation canbe on each quadrant by convolving input feature map data with a set ofN×M kernels to generate output feature maps. Node 0 data 1710 and node 1data 1720 are illustrated, with node 0 data 1710 including anindependent patch 1711, node 1 data 1720 including an independent patch1721, and each node having one or more halo regions 1707A-1707D having amulti-node data dependency. The multi-node data dependency of the haloregions 1707A-1707D can occur due to the convolution operation for thenode data and the N×M kernel 1713 crossing a node boundary 1712. Wherethe N×M kernel 1713 would cross the node boundary 1712, the convolutionoperation cannot be performed based on data stored entirely on a singlenode. Accordingly, to perform distributed training for a CNN usingfeature data partitioned along the X and Y dimension, a data transfer ofweight and/or feature map data may be required before the convolutioncan be performed on the various halo regions 1707A-1707D.

FIG. 17C is a sequence diagram of compute and data exchange, accordingto one embodiment. The data and compute patterns used for distributedtraining of a neural network can vary across embodiments andconfigurations. In one embodiment, multiple nodes 1760A-1760C canperform a first halo exchange 1772A in which halo region data isexchanged between nodes. In one embodiment, compute operations thatdepend upon exchanged data are performed after the halo region data isexchanged but before compute operations are performed for the non-haloregions. For example and in one embodiment, a dot product operation canbe performed on two distributed vectors, which returns the global sum ofall partial results. This operation can include the exchange of thepartial results over the network for at least a portion of thecomputations. Some compute operations may be blocked due to remote datadependencies. Accordingly, any calculations or data transfers requiredto satisfy a remote data dependency are scheduled as high priorityoperations.

Convolution operations using multi-dimensional (e.g., X and/or Y) datapartitioning may include the exchange of data between nodes before thecomplete set of computations can be performed. With the halo exchange1772A performed, each node can perform a set of compute operations1774A-1774C. The remote data dependencies for the compute operations1774A-1774C are satisfied by the halo exchange 1772A, enabling theentire set of compute operations to be performed. Before performingcompute operations for subsequent layers, an additional halo exchange1772B can be performed, followed by an additional round of computeoperations (not shown).

FIG. 17D is a sequence diagram of compute and data exchange, accordingto an additional embodiment. In one embodiment, compute andcommunication operations are performed in an overlapping manner. Insteadof performing an upfront halo exchange, a set of independent computeoperations 1776A-1776C can be performed in which calculations withoutremote data dependencies are performed instead of blocking calculationsuntil remote data dependencies are resolved. Data exchange 1780 and dataexchange 1781 can occur between nodes in parallel with the independentcompute operations 1776A-1776C. In one embodiment, once the computeoperations without remote data dependencies are complete the nodes willhave exchanged sufficient data to perform the halo compute operations1778A-1778C. In one embodiment, fine-grained data exchange isfacilitated by asynchronous communication logic within a communicationsmodule, such as the communications module 1517 of FIG. 15. Theasynchronous communication enabled by the communication module canenable overlapping compute and communication operations that efficientlyinterleave to optimize both compute and communication efficiency andthroughput. In one embodiment, the prioritized communication channels ofthe communication module enables prioritized resolution of contendingcommunication requests, such that, as the independent compute operations1776A-1776C begin to complete, any remaining data exchanges that wouldblocking the beginning of the halo compute operations 1778A-1778C areresolved

FIGS. 18-21 illustrate flow diagrams that describe operations to enabledistributed machine learning via the MLSL API. General operations fordata parallelism and halo exchange are illustrated in FIG. 18. MLSLsetup is illustrated in FIG. 19. Forward propagation is illustrated inFIG. 20. Backward propagation is illustrated in FIG. 21.

As shown in FIG. 18, data or hybrid parallelism and halo exchangeoperations include operations to multi-dimensionally partition featuremap data across multiple nodes for distributed training of aconvolutional neural network, as shown at block 1802. In one embodiment,k-dimensional feature maps may be split by as many as k dimensions. Forexample, a two-dimensional feature map may be split along two dimensionsor a single dimension, while a three-dimensional feature map may besplit along one, two, or three dimensions. As shown at block 1804, theoperations additionally include to perform data or hybrid parallelconvolution operations on the multiple partitions to train weight dataof the neural network. For distributed training, forward compute andbackward compute operations are performed ad described herein.Additionally, the operations include to exchange data between nodes toenable computation of halo regions, as shown at block 1806. Thecomputation and data exchange can be performed using one of multipletechniques. In one embodiment, halo exchange is performed at thebeginning of the compute operations for each layer to transfer or makeavailable data required to satisfy various remote dependencies acrossthe set of compute nodes, as shown in FIG. 17C. Alternatively,non-dependent compute operations can be performed in concert with dataexchanges that satisfy remote dependencies. While the operations aredescribed with respect to a CNN, the techniques described herein areapplicable to machine learning and distributed training in general.

An MLSL setup operation is performed to enable the use of MLSL to scaledistributed machine learning operations for the hybrid parallelism andhalo exchange for distributed machine learning techniques describedherein. As shown in FIG. 19, operations for MLSL setup include a firstoperation to initialize the MLSL library to enable use of the MLSL API,as shown at block 1902. The MLSL logic can then be used to create asession object and set a global mini-batch size, as shown at block 1904.The global mini-batch size can be determined based on the sum of localbatch sizes. The MLSL logic can then be used to create a distributionobject that indicates a number of partitions for data parallelism and anumber of partitions for model parallelism, as shown at block 1906.

The MLSL logic can then be used to create an operation object for eachlayer of the neural network, as shown at block 1908. Creating anoperation object for each layer, in one embodiment, include to create anauxiliary OperationRegInfo object that holds information about learnableparameters and activations shapes. The parameters define a specificrelationship between input and output activations and parameters ofoperation. The MLSL API enables the developer to add input/outputactivation shapes and shapes of parameters to the OperationRegInfoobject. Using the MLSL API, the developer can then create an operationobject, delete the delete OperationRegInfo object, and set dependenciesbetween operations. Using information about the batch size and shapes, adeveloper can then use the MLSL API to allocate buffers for gradientwith respect to parameters, input activation, and gradients with respectto the input activation. As linked operations share common activations,the link operations may be allocated on one side of a transaction andreused on the other size of the transaction. they should be allocatedonly on one side and reused on the other side). In one embodiment theMLSL library provides a dedicated allocator what enables specializedoptimizations. In one embodiment, the session object created at block1904 includes a commit method that may be used to finalize the creationof the Operation object.

The MLSL logic can then be used to perform machine learning frameworkworkflow with computational portions of the workflow wrapped with MLSLAPI calls, as shown at block 1910. In one embodiment the MLSL API callsenable automatic exchange of activations, gradients with respect toactivations and gradients with respect to parameters. The MLSL logic canthen be used to update parameters based on the machine learningframework workflow performed at block 1910.

During forward compute, the MLSL library can be used to fascilitate thetransfer of data associated with halo regions. In one embodiment, asshown in FIG. 20, logic 2000 enabled by the MLSL library can beconfigured to identify memory addresses of data associated with haloregions of a layer of a neural network, as shown at block 2002. Thepixels and associated addresses of the halo region can be determinedbased on the kernel/filter size and the size of the feature map sliceassigned to each node. For example, during a 1×1 convolution operation ahalo region may not arise, while a larger halo region may be present forlarger filter sizes. The logic 2000 can then determine a set of GPGPUcache lines associated with memory addresses of data associated withhalo regions, as shown at block 2004. The logic 2000, in one embodiment,can determine the relevant GPGPU cache lines by enabling a query ofcache control logic within the GPGPU. The GPGPU, in conjunction withlogic provided by the MLSL library can then enable training logic withina machine learning application to perform forward propagationcomputation, as shown at block 2006. During forward propagationcomputation, the logic 2000 can monitor the GPGPU cache lines associatedwith the identified memory addresses, as shown at block 2008. The logic2000 can then transfer halo data upon a cache line update, as shown atblock 2010. As the dependency information for the halo region data isknown, the logic 2000 can automatically transfer the halo data to theappropriate node that depends upon the computed data.

During the backward compute, the MLSL logic can enable the accelerationof weight data transfer. Communication can be enabled using a similarapproach as in the forward compute pass, taking into account thatcommunications occur backwards from a layer to the previous layer. Inone embodiment, as shown in FIG. 21, logic 2100 enabled by the MLSLlibrary can identify memory addresses of weight data to be distributedamong neural network nodes, as shown at block 2102. The logic 2100 canthen determine GPGPU cache lines associated with memory addresses ofweight data, as shown at block 2104. The logic 2100 can then performbackward propagation computation for the layer of the neural network, asshown at block 2016. The logic 2100 can then monitor GPGPU cache linesassociated with the identified memory addresses, as shown at block 2108,and automatically transfer weight data upon a cache line update, asshown at block 2110.

The logic 2000, 2100 enabled by the MLSL library, in one embodiment, isfirmware-based logic that is performed by a microcontroller within ageneral-purpose graphics processing unit as described herein. Suchmicrocontroller can be configured by the MLSL library to monitor GPGPUcache lines associated with identified memory addresses. In oneembodiment the MLSL library can interface with graphics controlsoftware, such as driver software for a GPGPU. The driver software canexecute on a general-purpose processor, application processor, orcentral processor that is in communication with the GPGPU.

FIG. 22 is a block diagram of a data processing system 2200, accordingto embodiments described herein. The data processing system 2200 is aheterogeneous processing system having a processor 2202, unified memory2210, and a GPGPU 2220 including machine learning acceleration logic.The processor 2202 and the GPGPU 2220 can be any of the processors andGPGPU/parallel processors as described herein. The processor 2202 canexecute instructions for a compiler 2215 stored in system memory 2212.The compiler 2215 executes on the processor 2202 to compile source code2214A into compiled code 2214B. The compiled code 2214B can include codethat may be executed by the processor 2202 and/or code that may beexecuted by the GPGPU 2220. During compilation, the compiler 2215 canperform operations to insert metadata, including hints as to the levelof data parallelism present in the compiled code 2214B and/or hintsregarding the data locality associated with threads to be dispatchedbased on the compiled code 2214B. The compiler 2215 can include theinformation necessary to perform such operations or the operations canbe performed with the assistance of a runtime library 2216, such as themachine learning scaling library (MLSL) described herein. The runtimelibrary 2216 can also facilitate the compiler 2215 in the compilation ofthe source code 2214A and includes instructions that are linked atruntime with the compiled code 2214B to facilitate execution of thecompiled instructions on the GPGPU 2220.

The unified memory 2210 represents a unified address space that may beaccessed by the processor 2202 and the GPGPU 2220. The unified memoryincludes system memory 2212 as well as GPGPU memory 2218. The GPGPUmemory 2218 includes GPGPU local memory 2228 within the GPGPU 2220 andcan also include some or all of system memory 2212. For example,compiled code 2214B stored in system memory 2212 can also be mapped intoGPGPU memory 2218 for access by the GPGPU 2220.

The GPGPU 2220 includes multiple compute blocks 2224A-2224N, which eachinclude one or more of the processing clusters 214A-214N or one or moreinstance of the processing array 212 as in FIG. 2. The GPGPU 2220 alsoincludes a set of registers 2224, cache memory 2226, and a power andperformance module 2225 that can be used as shared resources for thecompute blocks 2224A-2224N. The power and performance module 2225 can beconfigured to adjust power delivery and clock frequencies for thecompute blocks 2224A-2224N to power gate idle components within thecompute blocks 2224A-2224N under heavy workloads. The GPGPU 2220includes GPGPU local memory 2228, which is physical memory that shares agraphics card or multi-chip module with the GPGPU 2220.

In one embodiment the GPGPU 2220 includes graphics and computeacceleration logic including an instruction fetch and decode unit 2221,a scheduler unit 2222, and a machine learning fixed function unit 2223.The fetch and decode unit 2221 is a fetch and decode unit includes logicto fetch and decode instructions to be computed by the GPGPU 2220. Inone embodiment the executed instructions can sequence and/or serialize,via the scheduler unit 2222, a set of operations and/or micro-operationsto be performed via compute block 2224A-2224N and/or the machinelearning fixed function unit 2223.

In one embodiment the machine learning fixed function unit 2223 is anapplication specific integrated circuit explicitly and exclusivelyconfigured to perform a large number of parallel matrix multiplicationoperations. In one embodiment the machine learning fixed function unit2223 is configured to perform matrix multiplications for convolutionfilters having non power-of-two filter sizes. In one embodiment themachine learning fixed function unit 2223 is a field programmable gatearray (FPGA) that provides fixed function logic that can updated betweenworkloads.

In some embodiments the GPGPU 2220 includes an integrated fabricinterface 2230 and fabric interface cache 2232. In one embodiment theintegrated fabric interface 2230 additionally includes an MLSL fabricmodule 2231 that enables the fabric interface to provide hardwareacceleration for certain MLSL operations. The fabric interface 2230 canbe a variant of the high-performance communications fabric 1521 of FIG.15B. The fabric interface 2230 has an address space that is mapped to atleast a portion of the GPGPU local memory 2228 and in one embodiment canparticipate in the unified memory 2210 shared by the processor 2202 andthe GPGPU 2220. The fabric interface cache 2232 is used to cache datareceived from or to be transmitted to the communication fabric thatenables data communication between compute nodes. In one embodiment whencomputation results are computed by the GPGPU 2220 and stored within theGPGPU local memory 2228, the fabric interface 2230 can transmit the datato other compute nodes from the GPGPU local memory 2228. In suchembodiment, data is not required to be transmitted to the system memory2212 unless the data is required for use by an application executing onthe processor 2202. Additionally, the fabric interface cache 2232 can beconfigured to cache data associated with a halo region when such data iswritten to the GPGPU local memory 2228. Caching halo region data withinthe fabric interface cache 2232 can reduce latency associated withdistributing halo region data to different nodes via the fabricinterface 2230.

The MLSL fabric module 2231 is configured to facilitate low latencytransmission of data between nodes. In one embodiment the MLSL fabricmodule 2231 can receive a set of addresses within the GPGPU local memory2228 that are associated with data objects managed by the MLSL runtime(e.g., runtime library 2216). For example, an address range for anoutput buffer to store activation data to be generated by the GPGPU 2220can be provided to the MLSL fabric module 2231. The MLSL fabric module2231 can then be configured to monitor the address range for updates.When the address range receives a write of activation data or weightdata as a result of a computation performed by the GPGPU 2220, the MLSLfabric module 2231 can schedule a transfer directly to the fabricinterface 2230 to transfer the output activation data. In oneembodiment, configuring the MLSL fabric module 2231 to monitor anaddress range can also trigger the GPU to mark cache lines associatedwith a monitored address range, such that the MLSL fabric module 2231has notice of, for example, when an invalidate and writeback is pendingfor a cache line associated with a monitored address. In suchconfiguration, a writeback and data transfer can be triggeredimmediately upon an update to a monitored cache line and the data can beautomatically transferred to dependent compute nodes via the fabricinterface 2230.

The protocol supported by the fabric interface 2230 can vary. In oneembodiment the fabric interface 2230 is high-speed Ethernet interface.In one embodiment the fabric interface 2230 is an Omni-Path interconnectinterface. In one embodiment the fabric interface 2230 is an InfiniBandinterface. In one embodiment the fabric interface 2230 is an NvLinkinterface. Other fabric interface technologies may also be supported.

It is to be appreciated that a lesser or more equipped system than theexample described above may be preferred for certain implementations.Therefore, the configuration of the data processing system 2200 may varyacross implementations depending upon numerous factors, such as priceconstraints, performance requirements, technological improvements, orother circumstances. The embodiments described herein are may findextensive use within high-performance computing and machine learningtraining environments. Accordingly, the present description anticipatesthe data processing system 2200, and other data processing and computingsystems described herein to be implemented as a high-performance serveror server array within a distributed computing system. Such distributedcomputing system can be implemented within a datacenter or server farm.However, embodiments are not limited to such implementation, and thetechniques described herein may also find use in a large-scaledistributed compute system of lower performance devices, such as but notlimited to mobile or handheld devices, tablet computing devices, orconnected consumer electronic devices.

Additional Exemplary Graphics Processing System

Details of the embodiments described above can be incorporated withingraphics processing systems and devices described below. The graphicsprocessing system and devices of FIG. 23 through FIG. 36 illustratealternative systems and graphics processing hardware that can implementany and all of the techniques described above.

Additional Exemplary Graphics Processing System Overview

FIG. 23 is a block diagram of a processing system 2300, according to anembodiment. In various embodiments the system 2300 includes one or moreprocessors 2302 and one or more graphics processors 2308, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 2302 or processorcores 2307. In one embodiment, the system 2300 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 2300 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 2300 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 2300 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 2300 is a television or set topbox device having one or more processors 2302 and a graphical interfacegenerated by one or more graphics processors 2308.

In some embodiments, the one or more processors 2302 each include one ormore processor cores 2307 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 2307 is configured to process aspecific instruction set 2309. In some embodiments, instruction set 2309may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 2307 may each processa different instruction set 2309, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 2307may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 2302 includes cache memory 2304.Depending on the architecture, the processor 2302 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 2302. In some embodiments, the processor 2302 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 2307 using knowncache coherency techniques. A register file 2306 is additionallyincluded in processor 2302 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 2302.

In some embodiments, processor 2302 is coupled with a processor bus 2310to transmit communication signals such as address, data, or controlsignals between processor 2302 and other components in system 2300. Inone embodiment the system 2300 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 2316 and an Input Output(I/O) controller hub 2330. A memory controller hub 2316 facilitatescommunication between a memory device and other components of system2300, while an I/O Controller Hub (ICH) 2330 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 2316 is integrated within the processor.

Memory device 2320 can be a dynamic random-access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 2320 can operate as system memory for the system 2300, to storedata 2322 and instructions 2321 for use when the one or more processors2302 executes an application or process. Memory controller hub 2316 alsocouples with an optional external graphics processor 2312, which maycommunicate with the one or more graphics processors 2308 in processors2302 to perform graphics and media operations.

In some embodiments, ICH 2330 enables peripherals to connect to memorydevice 2320 and processor 2302 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 2346, afirmware interface 2328, a wireless transceiver 2326 (e.g., Wi-Fi,Bluetooth), a data storage device 2324 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 2340 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 2342 connect input devices, suchas keyboard and mouse 2344 combinations. A network controller 2334 mayalso couple with ICH 2330. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 2310. It willbe appreciated that the system 2300 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 2330may be integrated within the one or more processor 2302, or the memorycontroller hub 2316 and I/O controller hub 2330 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 2312.

FIG. 24 is a block diagram of an embodiment of a processor 2400 havingone or more processor cores 2402A-2402N, an integrated memory controller2414, and an integrated graphics processor 2408. Those elements of FIG.24 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor2400 can include additional cores up to and including additional core2402N represented by the dashed lined boxes. Each of processor cores2402A-2402N includes one or more internal cache units 2404A-2404N. Insome embodiments each processor core also has access to one or moreshared cached units 2406.

The internal cache units 2404A-2404N and shared cache units 2406represent a cache memory hierarchy within the processor 2400. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (T 3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 2406 and2404A-2404N.

In some embodiments, processor 2400 may also include a set of one ormore bus controller units 2416 and a system agent core 2410. The one ormore bus controller units 2416 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 2410 provides management functionality forthe various processor components. In some embodiments, system agent core2410 includes one or more integrated memory controllers 2414 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 2402A-2402Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 2410 includes components for coordinating andoperating cores 2402A-2402N during multi-threaded processing. Systemagent core 2410 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 2402A-2402N and graphics processor 2408.

In some embodiments, processor 2400 additionally includes graphicsprocessor 2408 to execute graphics processing operations. In someembodiments, the graphics processor 2408 couples with the set of sharedcache units 2406, and the system agent core 2410, including the one ormore integrated memory controllers 2414. In some embodiments, a displaycontroller 2411 is coupled with the graphics processor 2408 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 2411 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 2408 or system agent core 2410.

In some embodiments, a ring based interconnect unit 2412 is used tocouple the internal components of the processor 2400. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 2408 couples with the ring interconnect 2412 via an I/O link2413.

The exemplary I/O link 2413 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 2418, such as an eDRAM module.In some embodiments, each of the processor cores 2402A-2402N andgraphics processor 2408 use embedded memory modules 2418 as a sharedLast Level Cache.

In some embodiments, processor cores 2402A-2402N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 2402A-2402N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 2402A-2402Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 2402A-2402N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor2400 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 25 is a block diagram of a graphics processor 2500, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 2500 includesa memory interface 2514 to access memory. Memory interface 2514 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 2500 also includes a displaycontroller 2502 to drive display output data to a display device 2520.Display controller 2502 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 2500includes a video codec engine 2506 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 2500 includes a block imagetransfer (BLIT) engine 2504 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 2510. In someembodiments, GPE 2510 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 2512 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 2512 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 2515.While 3D pipeline 2512 can be used to perform media operations, anembodiment of GPE 2510 also includes a media pipeline 2516 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 2516 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2506. In some embodiments, media pipeline 2516 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 2515. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 2515.

In some embodiments, 3D/Media subsystem 2515 includes logic forexecuting threads spawned by 3D pipeline 2512 and media pipeline 2516.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 2515, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 2515 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Exemplary Additional Graphics Processing Engine

FIG. 26 is a block diagram of a graphics processing engine 2610 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 2610 is a version ofthe GPE 2510 shown in FIG. 25. Elements of FIG. 26 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 2512 and media pipeline 2516 of FIG. 25 are illustrated. Themedia pipeline 2516 is optional in some embodiments of the GPE 2610 andmay not be explicitly included within the GPE 2610. For example and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 2610.

In some embodiments, GPE 2610 couples with or includes a commandstreamer 2603, which provides a command stream to the 3D pipeline 2512and/or media pipelines 2516. In some embodiments, command streamer 2603is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 2603 receives commands from the memory and sends thecommands to 3D pipeline 2512 and/or media pipeline 2516. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 2512 and media pipeline 2516. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 2512 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 2512 and/or image data andmemory objects for the media pipeline 2516. The 3D pipeline 2512 andmedia pipeline 2516 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 2614.

In various embodiments the 3D pipeline 2512 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 2614. The graphics core array 2614 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 2614 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 2614 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 2307 of FIG. 23 or core 2402A-2402N as in FIG. 24.

Output data generated by threads executing on the graphics core array2614 can output data to memory in a unified return buffer (URB) 2618.The URB 2618 can store data for multiple threads. In some embodimentsthe URB 2618 may be used to send data between different threadsexecuting on the graphics core array 2614. In some embodiments the URB2618 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 2620.

In some embodiments, graphics core array 2614 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 2610. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 2614 couples with shared function logic 2620that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 2620 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 2614. In variousembodiments, shared function logic 2620 includes but is not limited tosampler 2621, math 2622, and inter-thread communication (ITC) 2623logic. Additionally, some embodiments implement one or more cache(s)2625 within the shared function logic 2620. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 2614. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 2620 and shared amongthe execution resources within the graphics core array 2614. The preciseset of functions that are shared between the graphics core array 2614and included within the graphics core array 2614 varies betweenembodiments.

FIG. 27 is a block diagram of another embodiment of a graphics processor2700. Elements of FIG. 27 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2700 includes a ringinterconnect 2702, a pipeline front-end 2704, a media engine 2737, andgraphics cores 2780A-2780N. In some embodiments, ring interconnect 2702couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2700 receives batches ofcommands via ring interconnect 2702. The incoming commands areinterpreted by a command streamer 2703 in the pipeline front-end 2704.In some embodiments, graphics processor 2700 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2780A-2780N. For 3D geometry processing commands,command streamer 2703 supplies commands to geometry pipeline 2736. Forat least some media processing commands, command streamer 2703 suppliesthe commands to a video front-end 2734, which couples with a mediaengine 2737. In some embodiments, media engine 2737 includes a VideoQuality Engine (VQE) 2730 for video and image post-processing and amulti-format encode/decode (MFX) 2733 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2736 and media engine 2737 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2780A.

In some embodiments, graphics processor 2700 includes scalable threadexecution resources featuring modular cores 2780A-2780N (sometimesreferred to as core slices), each having multiple sub-cores 2750A-550N,2760A-2760N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2700 can have any number of graphicscores 2780A through 2780N. In some embodiments, graphics processor 2700includes a graphics core 2780A having at least a first sub-core 2750Aand a second sub-core 2760A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2750A).In some embodiments, graphics processor 2700 includes multiple graphicscores 2780A-2780N, each including a set of first sub-cores 2750A-2750Nand a set of second sub-cores 2760A-2760N. Each sub-core in the set offirst sub-cores 2750A-2750N includes at least a first set of executionunits 2752A-2752N and media/texture samplers 2754A-2754N. Each sub-corein the set of second sub-cores 2760A-2760N includes at least a secondset of execution units 2762A-2762N and samplers 2764A-2764N. In someembodiments, each sub-core 2750A-2750N, 2760A-2760N shares a set ofshared resources 2770A-2770N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Exemplary Additional Execution Units

FIG. 28 illustrates thread execution logic 2800 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 28 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 2800 includes a shaderprocessor 2802, a thread dispatcher 2804, instruction cache 2806, ascalable execution unit array including a plurality of execution units2808A-2808N, a sampler 2810, a data cache 2812, and a data port 2814. Inone embodiment the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 2808A, 2808B, 2808C, 2808D, through 2808N-1 and 2808N)based on the computational requirements of a workload. In one embodimentthe included components are interconnected via an interconnect fabricthat links to each of the components. In some embodiments, threadexecution logic 2800 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache2806, data port 2814, sampler 2810, and execution units 2808A-2808N. Insome embodiments, each execution unit (e.g. 2808A) is a stand-aloneprogrammable general purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In variousembodiments, the array of execution units 2808A-2808N is scalable toinclude any number individual execution units.

In some embodiments, the execution units 2808A-2808N are primarily usedto execute shader programs. A shader processor 2802 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 2804. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units2808A-2808N. For example, the geometry pipeline (e.g., 2736 of FIG. 27)can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 2800 (FIG. 28) for processing. In some embodiments,thread dispatcher 2804 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 2808A-2808N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 2808A-2808N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units2808A-2808N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 2808A-2808N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) orFloating-Point Units (FPUs) for a particular graphics processor. In someembodiments, execution units 2808A-2808N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 2806) are included in thethread execution logic 2800 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2812) are included to cache thread data during thread execution. In someembodiments, a sampler 2810 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 2810 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2800 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor2802 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 2802 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 2802dispatches threads to an execution unit (e.g., 2808A) via threaddispatcher 2804. In some embodiments, pixel shader 2802 uses texturesampling logic in the sampler 2810 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 2814 provides a memory accessmechanism for the thread execution logic 2800 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 2814 includes or couples to one or more cachememories (e.g., data cache 2812) to cache data for memory access via thedata port.

FIG. 29 is a block diagram illustrating a graphics processor instructionformats 2900 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2900 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 2910. A 64-bitcompacted instruction format 2930 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 2910 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 2930. The native instructions availablein the 64-bit format 2930 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 2913. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format2910.

For each format, instruction opcode 2912 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2914 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 2910 an exec-size field2916 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 2916 is not available foruse in the 64-bit compact instruction format 2930.

Some execution unit instructions have up to three operands including twosource operands, src0 2920, src1 2922, and one destination 2918. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2924), where the instructionopcode 2912 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2910 includes anaccess/address mode field 2926 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 2910 includes anaccess/address mode field 2926, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 2926 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 2912bit-fields to simplify Opcode decode 2940. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2942 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 2942 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 2944 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 2946 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 2948 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 2948 performs the arithmetic operations in parallelacross data channels. The vector math group 2950 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Exemplary Additional Graphics Pipeline

FIG. 30 is a block diagram of another embodiment of a graphics processor3000. Elements of FIG. 30 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 3000 includes a graphicspipeline 3020, a media pipeline 3030, a display engine 3040, threadexecution logic 3050, and a render output pipeline 3070. In someembodiments, graphics processor 3000 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 3000 via a ring interconnect 3002. In someembodiments, ring interconnect 3002 couples graphics processor 3000 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 3002 areinterpreted by a command streamer 3003, which supplies instructions toindividual components of graphics pipeline 3020 or media pipeline 3030.

In some embodiments, command streamer 3003 directs the operation of avertex fetcher 3005 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 3003. In someembodiments, vertex fetcher 3005 provides vertex data to a vertex shader3007, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 3005 andvertex shader 3007 execute vertex-processing instructions by dispatchingexecution threads to execution units 3052A-3052B via a thread dispatcher3031.

In some embodiments, execution units 3052A-3052B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 3052A-3052B have anattached L1 cache 3051 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 3020 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 3013 operatesat the direction of hull shader 3011 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 3020. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 3011, tessellator 3013, and domain shader 3017) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 3019 via one or more threads dispatched to executionunits 3052A-3052B, or can proceed directly to the clipper 3029. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader3019 receives input from the vertex shader 3007. In some embodiments,geometry shader 3019 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 3029 processes vertex data. The clipper3029 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 3073 in the render output pipeline3070 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 3050. In some embodiments, anapplication can bypass the rasterizer and depth test component 3073 andaccess un-rasterized vertex data via a stream out unit 3023.

The graphics processor 3000 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 3052A-3052B and associated cache(s) 3051,texture and media sampler 3054, and texture/sampler cache 3058interconnect via a data port 3056 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 3054, caches 3051, 3058 and execution units3052A-3052B each have separate memory access paths.

In some embodiments, render output pipeline 3070 contains a rasterizerand depth test component 3073 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache3078 and depth cache 3079 are also available in some embodiments. Apixel operations component 3077 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 3041, or substituted at display time by the displaycontroller 3043 using overlay display planes. In some embodiments, ashared L3 cache 3075 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 3030 includes amedia engine 3037 and a video front-end 3034. In some embodiments, videofront-end 3034 receives pipeline commands from the command streamer3003. In some embodiments, media pipeline 3030 includes a separatecommand streamer. In some embodiments, video front-end 3034 processesmedia commands before sending the command to the media engine 3037. Insome embodiments, media engine 3037 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic3050 via thread dispatcher 3031.

In some embodiments, graphics processor 3000 includes a display engine3040. In some embodiments, display engine 3040 is external to processor3000 and couples with the graphics processor via the ring interconnect3002, or some other interconnect bus or fabric. In some embodiments,display engine 3040 includes a 2D engine 3041 and a display controller3043. In some embodiments, display engine 3040 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 3043 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 3020 and media pipeline 3030 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 31A is a block diagram illustrating a graphics processor commandformat 3100 according to some embodiments. FIG. 31B is a block diagramillustrating a graphics processor command sequence 3110 according to anembodiment. The solid lined boxes in FIG. 31A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 3100 of FIG. 31A includes data fields to identify atarget client 3102 of the command, a command operation code (opcode)3104, and the relevant data 3106 for the command. A sub-opcode 3105 anda command size 3108 are also included in some commands.

In some embodiments, client 3102 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 3104 and, if present, sub-opcode 3105 to determine theoperation to perform. The client unit performs the command usinginformation in data field 3106. For some commands an explicit commandsize 3108 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 31B shows an exemplary graphics processorcommand sequence 3110. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 3110 maybegin with a pipeline flush command 3112 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 3122 and the media pipeline 3124 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 3112 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 3113 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 3113is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 3112 isrequired immediately before a pipeline switch via the pipeline selectcommand 3113.

In some embodiments, a pipeline control command 3114 configures agraphics pipeline for operation and is used to program the 3D pipeline3122 and the media pipeline 3124. In some embodiments, pipeline controlcommand 3114 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 3114 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 3116 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 3116 includes selecting the size and number ofreturn buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 3120,the command sequence is tailored to the 3D pipeline 3122 beginning withthe 3D pipeline state 3130 or the media pipeline 3124 beginning at themedia pipeline state 3140.

The commands to configure the 3D pipeline state 3130 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 3130 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 3132 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 3132 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 3132command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 3132 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 3122 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 3122 is triggered via an execute 3134command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 3110follows the media pipeline 3124 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 3124 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 3124 is configured in a similarmanner as the 3D pipeline 3122. A set of commands to configure the mediapipeline state 3140 are dispatched or placed into a command queue beforethe media object commands 3142. In some embodiments, media pipelinestate commands 3140 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,media pipeline state commands 3140 also support the use of one or morepointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 3142 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 3142. Once the pipeline state is configured andmedia object commands 3142 are queued, the media pipeline 3124 istriggered via an execute command 3144 or an equivalent execute event(e.g., register write). Output from media pipeline 3124 may then be postprocessed by operations provided by the 3D pipeline 3122 or the mediapipeline 3124. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 32 illustrates exemplary graphics software architecture for a dataprocessing system 3200 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application3210, an operating system 3220, and at least one processor 3230. In someembodiments, processor 3230 includes a graphics processor 3232 and oneor more general-purpose processor core(s) 3234. The graphics application3210 and operating system 3220 each execute in the system memory 3250 ofthe data processing system.

In some embodiments, 3D graphics application 3210 contains one or moreshader programs including shader instructions 3212. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 3214 in a machinelanguage suitable for execution by the general-purpose processor core3234. The application also includes graphics objects 3216 defined byvertex data.

In some embodiments, operating system 3220 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 3220 can support agraphics API 3222 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 3220uses a front-end shader compiler 3224 to compile any shader instructions3212 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 3210. In some embodiments, the shader instructions 3212 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 3226 contains a back-endshader compiler 3227 to convert the shader instructions 3212 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 3212 in the GLSL high-level language are passed to a usermode graphics driver 3226 for compilation. In some embodiments, usermode graphics driver 3226 uses operating system kernel mode functions3228 to communicate with a kernel mode graphics driver 3229. In someembodiments, kernel mode graphics driver 3229 communicates with graphicsprocessor 3232 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 33 is a block diagram illustrating an IP core development system3300 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system3300 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility3330 can generate a software simulation 3310 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation3310 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 3312. The simulation model 3312 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 3315 can then be created or synthesized from thesimulation model 3312. The RTL design 3315 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 3315, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 3315 or equivalent may be further synthesized by thedesign facility into a hardware model 3320, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 3365 using non-volatile memory 3340 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 3350 or wireless connection 3360. Thefabrication facility 3365 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 34-36 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 34 is a block diagram illustrating an exemplary system on a chipintegrated circuit 3400 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 3400includes one or more application processor(s) 3405 (e.g., CPUs), atleast one graphics processor 3410, and may additionally include an imageprocessor 3415 and/or a video processor 3420, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 3400 includes peripheral or bus logic including a USBcontroller 3425, UART controller 3430, an SPI/SDIO controller 3435, andan I²S/I²C controller 3440. Additionally, the integrated circuit caninclude a display device 3445 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 3450 and a mobileindustry processor interface (MIPI) display interface 3455. Storage maybe provided by a flash memory subsystem 3460 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 3465 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine3470.

FIG. 35 is a block diagram illustrating an exemplary graphics processor3510 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 3510 can be a variant of the graphics processor 3410 of FIG.34. Graphics processor 3510 includes a vertex processor 3505 and one ormore fragment processor(s) 3515A-3515N (e.g., 3515A, 3515B, 3515C,3515D, through 3515N-1, and 3515N). Graphics processor 3510 can executedifferent shader programs via separate logic, such that the vertexprocessor 3505 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 3515A-3515Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 3505 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 3515A-3515N use the primitiveand vertex data generated by the vertex processor 3505 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 3515A-3515N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 3510 additionally includes one or more memorymanagement units (MMUs) 3520A-3520B, cache(s) 3525A-3525B, and circuitinterconnect(s) 3530A-3530B. The one or more MMU(s) 3520A-3520B providefor virtual to physical address mapping for graphics processor 3510,including for the vertex processor 3505 and/or fragment processor(s)3515A-3515N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 3525A-3525B. In one embodiment the one or more MMU(s)3520A-3520B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 3405, image processor 3415, and/or video processor 3420 ofFIG. 34, such that each processor 3405-3420 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 3530A-3530B enable graphics processor 3510 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 36 is a block diagram illustrating an additional exemplary graphicsprocessor 3610 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 3610 can be a variant of the graphics processor 3410of FIG. 34. Graphics processor 3610 includes the one or more MMU(s)3620A-3620B, caches 3625A-3625B, and circuit interconnects 3630A-3630Bof the integrated circuit 3600 of FIG. 36.

Graphics processor 3610 includes one or more shader core(s) 3615A-3615N(e.g., 3615A, 3615B, 3615C, 3615D, 3615E, 3615F, through 3615N-1, and3615N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 3610 includes an inter-core taskmanager 3605, which acts as a thread dispatcher to dispatch executionthreads to one or more shader cores 3615A-3615N and a tiling unit 3618to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

Embodiments described herein can be implemented as any one or acombination of: one or more microchips or integrated circuitsinterconnected using a parent-board, hardwired logic, software stored bya memory device and executed by a microprocessor, firmware, anapplication specific integrated circuit (ASIC), and/or a fieldprogrammable gate array (FPGA). The term “logic” may include, by way ofexample, software or hardware and/or combinations of software andhardware.

Embodiments may be provided, for example, as a computer program productwhich may include one or more machine-readable media having storedthereon machine-executable instructions that, when executed by one ormore machines such as a computer, network of computers, or otherelectronic devices, may result in the one or more machines carrying outoperations in accordance with embodiments described herein. Amachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), andmagneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable ReadOnly Memories), EEPROMs (Electrically Erasable Programmable Read OnlyMemories), magnetic or optical cards, flash memory, or other type ofnon-transitory machine-readable media suitable for storingmachine-executable instructions.

The following clauses and/or examples pertain to specific embodiments orexamples thereof. Specifics in the examples may be used anywhere in oneor more embodiments. The various features of the different embodimentsor examples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system according toembodiments and examples described herein. Various components can be ameans for performing the operations or functions described.

With respect to the embodiments described herein, a system of one ormore computers can be configured to perform particular operations oractions under the direction of software, firmware, hardware, or acombination of such installed on the system. One or more computerprograms can be configured to perform particular operations or actionsbased on instructions that, when executed by a data processingapparatus, cause the data processing apparatus to perform the actionsindicated actions.

One embodiment provides for a system to compute and distribute data fordistributed training of a neural network, the system comprising a systemmemory to store a set of trainable machine learning parameters and alibrary to facilitate data transmission during distributed training ofthe neural network; a fabric interface to enable transmission andreceipt of data associated with the set of trainable machine learningparameters; a first set of general-purpose processor cores to executeinstructions provided by the library, the instructions to control a datatransmission library; and a general-purpose graphics processor toperform compute operations associated with machine learning frameworkworkflow to generate gradient data for the trainable machine learningparameters, wherein the first set of general-purpose processor cores areto control the data transmission library to send and receive trainingdata via the fabric interface during the machine learning frameworkworkflow.

One embodiment provides for a general-purpose graphics processing unitcomprising a processing array including multiple compute blocks, eachcompute block including multiple processing clusters; a memory module;and a fabric interface to facilitate data exchange with an externalprocessor over a communication fabric, the fabric interface to transferdata stored in the memory module to an external processor to enablecomputation operations on a halo region, the compute operations on thehalo region having a dependency on data stored in the memory module. Inone embodiment the general-purpose graphics processing unit additionallyincludes an instruction fetch unit to fetch an instruction forprocessing via the processing array and a scheduler unit to schedule theinstruction for processing via the processing array, the scheduler unitto schedule a set of instructions to the processing array to cause theprocessing array to accelerate distributed training of a neural network,the instructions to cause the processing array to perform a parallelconvolution operation on a partition of a feature map associated with aneural network to generate intermediate data; storing the intermediatedata within the memory module; and transferring the intermediate datavia the fabric interface, the intermediate data including a dependencyof the compute operation on the halo region.

In one embodiment the general-purpose graphics processing unit isadditionally multi-dimensionally partition the feature map associatedwith a neural network in a horizontal and vertical dimension (e.g., Xand Y dimension) and optionally in a depth or z dimension. Thepartitioned feature map can then be transmitted to the eternal processorvia the fabric interface. In addition or as an alternative to the fabricinterface internal to the GPGPU, an external fabric interface or networkdevice may also be used to transmit data. The processing array cantransmit, via the internal fabric interface, external fabric interface,or other communications link, a partition of the feature map to theexternal processor. In one embodiment the external processor is externalto the GPGPU, such as an additional GPGPU within a multi-GPGPU dataprocessing system, or another parallel processor in a heterogeneousparallel processing system. In one embodiment the external processor isexternal to a data processing system housing the GPGPU, and may beincluded in a compute node that is external to and communicativelyattached to the data processing system housing the GPGPU. The internaland/or external fabric interconnect may be one or more of multiplehigh-speed network connection technologies, including but not limited toPCI Express, Ethernet, Fiber Channel, InfiniBand, Omni-PathInterconnect, or proprietary technologies such as NvLink.

In one embodiment the general-purpose graphics processing unit includesa cache memory to cache data stored in the memory module and logicconfigurable to monitor a cache line within the cache memory. The cachememory can be a general GPGPU cache memory or a cache memory associatedwith the fabric interface. The monitored cache line is associated with amemory address within the memory module storing data associated with thedependency of the compute operation on the halo region. The dataassociated with the dependency of the compute operation of the haloregion can include, in one embodiment, the data upon which the computeof the halo region depends and/or location or address information forthe node that has the dependency. The general-purpose graphicsprocessing unit can then be configured to automatically transfer datawithin the monitored cache line via the fabric interface upon detectionof an update to the data within the cache line. The monitoring logic canconfigure the GPGPU to automatically transfer data to the appropriatenode within the distributed training system.

One embodiment provides a data processing system to accelerate adistributed training operation for a neural network, the data processingsystem comprising a heterogeneous processing system including ageneral-purpose processor and a general-purpose graphics processor; amemory module; and a non-transitory machine-readable medium to storeinstructions for execution by the heterogeneous processing system. Theinstructions can configure or cause the heterogeneous processing systemto identify a memory address within the memory module associated with ahalo region of a layer of a neural network, wherein the halo region is aregion of the layer of the neural network in which compute operationshave a dependency on data associated with multiple different nodes ofthe distributed training operation; determine a cache line within thegeneral-purpose graphics processor associated with the memory address;monitor the cache line for updates to the data associated with the haloregion; and automatically transfer the updates to the data associatedwith the halo region to a node of the distributed training operationhaving a dependency upon the data. In one embodiment the heterogeneousprocessing system is to perform a forward propagation computation forthe layer of the neural network to update the data associated with thehalo region and perform a backward propagation computation for the layerof the neural network to update the data associated with the haloregion. In one embodiment the general-purpose graphics processorincludes a fabric interface to transfer the updates to the dataassociated with the halo region. In various embodiments thegeneral-purpose graphics processor can also perform any of the GPGPUoperations described herein.

One embodiments provides a method of transmitting data between multiplecompute nodes of a distributed training system for a neural network, themethod comprising multi-dimensionally partitioning data of a feature mapinto multiple partitions; distributing the multiple partitions acrossmultiple nodes of the distributed training system; performing adistributed parallel convolution operation on the multiple partitions totrain weight data of the neural network; and exchanging data betweennodes to enable a compute operation for a halo region, the halo regionhaving dependency on data processed by a different node. In oneembodiment the distributed parallel convolution operation is a dataparallel convolution operation or a hybrid parallel convolutionoperation. In one embodiment, exchanging data between nodes beforeperforming compute operations on the halo region and performing computeoperations on the halo region before performing compute operations on anon-halo region. In one embodiment the method additionally includesperforming compute operations on the non-halo region before performingcompute operations on the halo region and exchanging data between nodeswhile performing compute operations on the non-halo region. In oneembodiment, compute and communication operations can be interleavedwhile performing compute operations on the non-halo region.

Those skilled in the art will appreciate from the foregoing descriptionthat the broad techniques of the embodiments can be implemented in avariety of forms. Therefore, while the embodiments have been describedin connection with particular examples thereof, the true scope of theembodiments should not be so limited since other modifications willbecome apparent to the skilled practitioner upon a study of thedrawings, specification, and following claims.

What is claimed is:
 1. A method of transmitting data between multiplecompute nodes of a distributed training system for a neural network, themethod comprising: multi-dimensionally partitioning data of a featuremap into multiple partitions; distributing the multiple partitionsacross multiple nodes of the distributed training system; performing adistributed parallel convolution operation on the multiple partitions totrain weight data of the neural network; exchanging data between nodesto enable a compute operation for a halo region, the halo region havingdependency on data processed by a different node, the data exchangedbetween the nodes before performing compute operations on the haloregion; and performing the compute operations on the halo region beforeperforming compute operations on a non-halo region.
 2. The method as inclaim 1, wherein multi-dimensionally partitioning data of a feature mapacross multiple nodes includes partitioning the data of the feature mapin a horizontal and vertical dimension.
 3. The method as in claim 2,wherein multi-dimensionally partitioning data of the feature map acrossmultiple nodes additionally includes partitioning data of the featuremap in a depth or Z dimension.
 4. The method as in claim 1, wherein thedistributed parallel convolution operation is a data parallelconvolution operation or a hybrid parallel convolution operation.
 5. Themethod as in claim 1, wherein the distributed parallel convolutionoperation is a model parallel convolution operation.
 6. The method as inclaim 4, additionally comprising exchanging data between nodes whileperforming compute operations on the non-halo region.
 7. The method asin claim 6, additionally comprising interleaving compute andcommunication operations while performing compute operations on thenon-halo region.
 8. A data processing system to accelerate a distributedtraining operation for a neural network, the data processing systemcomprising: a heterogeneous processing system including ageneral-purpose processor and a general-purpose graphics processor; amemory module; a non-transitory machine-readable medium to storeinstructions for execution by the heterogeneous processing system tocause the heterogeneous processing system to: identify a memory addresswithin the memory module associated with a halo region of a layer of aneural network, wherein the halo region is a region of the layer of theneural network in which compute operations have a dependency on dataassociated with multiple different nodes of the distributed trainingoperation; determine a cache line within the general-purpose graphicsprocessor associated with the memory address; monitor the cache line forupdates to the data associated with the halo region; and automaticallytransfer the updates to the data associated with the halo region to anode of the distributed training operation having a dependency upon thedata.
 9. A data processing system as in claim 8, the heterogeneousprocessing system to: perform a forward propagation computation for thelayer of the neural network to update the data associated with the haloregion; and perform a backward propagation computation for the layer ofthe neural network to update the data associated with the halo region.10. The data processing system as in claim 9, the general-purposegraphics processor including a fabric interface to transfer the updatesto the data associated with the halo region.
 11. A non-transitorymachine-readable medium storing instructions which, when executed by oneor more processors, cause the one or more processors to performoperations comprising: multi-dimensionally partitioning data of afeature map into multiple partitions; distributing the multiplepartitions across multiple nodes of a distributed training system for aneural network; performing a distributed parallel convolution operationon the multiple partitions to train weight data of the neural network;exchanging data between nodes to enable a compute operation for a haloregion, the halo region having dependency on data processed by adifferent node, the data exchanged between the nodes before performingcompute operations on the halo region; and performing the computeoperations on the halo region before performing compute operations on anon-halo region.
 12. The non-transitory machine-readable medium as inclaim 11, wherein multi-dimensionally partitioning data of a feature mapacross multiple nodes includes partitioning the data of the feature mapin a horizontal and vertical dimension.
 13. The non-transitorymachine-readable medium as in claim 12, wherein multi-dimensionallypartitioning data of the feature map across multiple nodes additionallyincludes partitioning data of the feature map in a depth or Z dimension.14. The non-transitory machine-readable medium as in claim 11, whereinthe distributed parallel convolution operation is a data parallelconvolution operation or a hybrid parallel convolution operation. 15.The non-transitory machine-readable medium as in claim 11, wherein thedistributed parallel convolution operation is a model parallelconvolution operation.
 16. The non-transitory machine-readable medium asin claim 14, the operations additionally comprising exchanging databetween nodes while performing compute operations on the non-haloregion.
 17. The non-transitory machine-readable medium as in claim 16,the operations additionally comprising interleaving compute andcommunication operations while performing compute operations on thenon-halo region.